A magnetic field prediction method based on UNet++ with the addition of inception module-Reference-Cited by-同舟云学术

A magnetic field prediction method based on UNet++ with the addition of inception module

Published:2024-09-10 Issue:17 Volume:21 Page:20240398-20240398
ISSN:1349-2543
Container-title:IEICE Electronics Express
language:en
Short-container-title:IEICE Electron. Express

Author:

Lin Shan¹,Gao Jie¹,Jin Liang²,Yang Yu²

Affiliation:

1. Guangzhou Metro Design&Research Institute Co., Ltd

2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Link

https://www.jstage.jst.go.jp/article/elex/21/17/21_21.20240398/_pdf

Reference30 articles.

1. [1] L. Jin, et al.: “A parallel computing method for electromagnetic problems based on cloud computing,” Transactions of China Electrotechnical Society 31 (2016) 5 (DOI: 10.19595/j.cnki.1000-6753.tces.2016.22.002).

2. [2] W. Zheng and Z. Cheng: “An inner-constrained separation technique for 3-D finite-element modeling of grain-oriented silicon steel laminations,” IEEE Trans. Magn. 48 (2012) 2277 (DOI: 10.1109/TMAG.2012.2191591).

3. [3] I.B. Senkyire and Z. Liu: “Supervised and semi-supervised methods for abdominal organ segmentation: a review,” International Journal of Automation and Computing 18 (2021) 887 (DOI: 10.1007/s11633-021-1313-0).

4. [4] M.D. Ribeiro, et al.: “DeepCFD: efficient steady-state laminar flow approximation with deep convolutional neural networks,” arXiv preprint (2020) arXiv: 2004.08826 (DOI: 10.48550/arXiv.2004.08826).

5. [5] X.S. Shi, et al.: “Loss-based attention for interpreting image-level prediction of convolutional neural networks,” IEEE Trans. Image Process. 30 (2021) 1662 (DOI: 10.1109/TIP.2020.3046875).